JOURNAL ARTICLE

Embedded adaptive mutation evolutionary programming for distributed generation management

Muhammad Fathi Mohd ZulkefliIsmail MusirinShahrizal JelaniMohd Helmi MansorNaeem M. S. Honnoon

Year: 2019 Journal:   Indonesian Journal of Electrical Engineering and Computer Science Vol: 16 (1)Pages: 364-364   Publisher: Institute of Advanced Engineering and Science (IAES)

Abstract

<span>Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced.</span>

Keywords:
Sizing Evolutionary programming Distributed generation Mathematical optimization Computer science Power (physics) Renewable energy Adaptive mutation Electric power system Dependency (UML) Genetic algorithm Engineering Electrical engineering Mathematics

Metrics

2
Cited By
0.17
FWCI (Field Weighted Citation Impact)
25
Refs
0.52
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Microgrid Control and Optimization
Physical Sciences →  Engineering →  Control and Systems Engineering
Smart Grid Energy Management
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Electric Power System Optimization
Physical Sciences →  Engineering →  Electrical and Electronic Engineering

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